class_docstr: Principal Component Analysis (PCA).
  
  Parameters
  ----------
  rescale_with_mean
      Whether or not to subtract each column's mean before performing SVD.
  rescale_with_std
      Whether or not to standardize each column before performing SVD.
  n_components
      The number of principal components to compute.
  n_iter
      The number of iterations used for computing the SVD.
  copy
      Whether nor to perform the computations inplace.
  check_input
      Whether to check the coherence of the inputs or not.
docstr: Calculate correlations between variables and components.
  
  The correlation between a variable and a component estimates the information they share. In
  the PCA framework, this correlation is called a loading.
  
  Note that the sum of the squared coefficients of correlation between a variable and all the
  components is equal to 1. As a consequence, the squared loadings are easier to interpret
  than the loadings (because the squared loadings give the proportion of the variance of the
  variables explained by the components).
docstr_tok: ["Calculate","correlations","between","variables","and","components",".","The","correlation","between","a","variable","and","a","component","estimates","the","information","they","share",".","In","the","PCA","framework",",","this","correlation","is","called","a","loading",".","Note","that","the","sum","of","the","squared","coefficients","of","correlation","between","a","variable","and","all","the","components","is","equal","to","1",".","As","a","consequence",",","the","squared","loadings","are","easier","to","interpret","than","the","loadings","(","because","the","squared","loadings","give","the","proportion","of","the","variance","of","the","variables","explained","by","the","components",")","."]
code: def column_correlations(self):
      """Calculate correlations between variables and components.
  
      The correlation between a variable and a component estimates the information they share. In
      the PCA framework, this correlation is called a loading.
  
      Note that the sum of the squared coefficients of correlation between a variable and all the
      components is equal to 1. As a consequence, the squared loadings are easier to interpret
      than the loadings (because the squared loadings give the proportion of the variance of the
      variables explained by the components).
  
      """
      return self.column_coordinates_.div(
          self._column_dist**0.5, axis=0
      )
  
imports: from __future__ import annotations
  import functools
  import altair
  import numpy
  import pandas
  import sklearn.base
  import sklearn.utils
  from sklearn import preprocessing
  from prince import svd, utils
label_desc: Use image node_id 12 for calling the PCA obj's underlying member method code with example usage: obj.column_correlations() and returns: self